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Abstract:

There is provided an information processing apparatus including an
acquisition unit acquiring information showing at least one of an
indicated party, who is a person or group indicated by a user, and
indicated content, which is content indicated by the user, and a
recommendation unit recommending, to the user, content that is similar to
at least one of content related to the indicated party, the indicated
content, and content liked by the user.

Claims:

1. An information processing apparatus comprising: an acquisition unit
acquiring information showing at least one of an indicated party, who is
a person or group indicated by a user, and indicated content, which is
content indicated by the user; and a recommendation unit recommending, to
the user, content that is similar to at least one of content related to
the indicated party, the indicated content, and content liked by the
user.

2. An information processing apparatus according to claim 1, further
comprising: a vector combining unit generating a combined vector by
combining an indicated characteristic vector, which is a characteristic
vector showing characteristics of the content related to the indicated
party or the indicated content, and a user taste vector, which shows
characteristics of the content liked by the user, wherein the
recommendation unit recommends, to the user, content whose characteristic
vector is similar to the combined vector.

3. An information processing apparatus according to claim 2, wherein the
vector combining unit combines the indicated characteristic vector and
the user taste vector using a ratio indicated by the user.

4. An information processing apparatus according to claim 3, further
comprising: a display control unit controlling display of a setting
screen which displays, when the user has made a total of at least two
indications of indicated parties and/or indicated content, displays
respectively corresponding to the indicated parties and/or the indicated
content and a display corresponding to the user at specified display
positions and which sets a ratio for use when combining the indicated
characteristic vector and the user taste vector, based on distances from
the respective display positions to a position indicated by the user.

5. An information processing apparatus according to claim 3, further
comprising: a display control unit operable, when the indicated party has
been indicated, to carry out control to display a name of the indicated
party in a setting screen setting the ratio for use when combining the
indicated characteristic vector and the user taste vector, wherein the
vector combining unit combines the indicated characteristic vector
showing the characteristics of the content related to the indicated party
and the user taste vector using the ratio set in the setting screen.

7. An information processing apparatus according to claim 6, further
comprising: a representative work extracting unit extracting a
representative work out of the content related to the indicated party
based on at least one of the number of multiple registrations of content
and user evaluations of content, wherein the indicated characteristic
vector generating unit generates the indicated characteristic vector for
the indicated party based on a characteristic vector of the extracted
representative work.

8. An information processing apparatus according to claim 2, wherein the
recommendation unit generates a list of content recommended to the user
and sets an order of content in the list based on similarity between the
combined vector and respective characteristic vectors of the content.

9. An information processing apparatus according to claim 1, wherein the
recommendation unit generates a list of content recommended to the user,
and the information processing apparatus further includes a
representative work extracting unit extracting a representative work out
of content related to one of the indicated party and a person or group
related to the indicated content, based on at least one of the number of
multiple registrations of content and user evaluations of content; and a
representative work inserting unit inserting the extracted representative
work at or near the top of the list.

10. An information processing apparatus according to claim 9, wherein the
representative work extracting unit extracts the representative work
separately for specified regions.

11. An information processing method carried out by an information
processing apparatus that recommends content, comprising: acquiring
information showing at least one of an indicated party, who is a person
or group indicated by a user, and indicated content, which is content
indicated by the user; and recommending, to the user, content that is
similar to at least one of content related to the indicated party, the
indicated content, and content liked by the user.

12. A program causing a computer to execute processing comprising:
acquiring information showing at least one of an indicated party, who is
a person or group indicated by a user, and indicated content, which is
content indicated by the user; and recommending, to the user, content
that is similar to at least one of content related to the indicated
party, the indicated content, and content liked by the user.

13. An information processing system including a server and a client,
wherein the client includes a transmission unit transmitting information
showing at least one of an indicated party, who is a person or group
indicated by a user, and indicated content, which is content indicated by
the user; and the server includes a reception unit receiving the
information transmitted from the client; and a recommendation unit
recommending, to the user, content that is similar to at least one of
content related to the indicated party, the indicated content, and
content liked by the user.

14. An information processing method comprising: a client transmitting
information showing at least one of an indicated party, who is a person
or group indicated by a user, and indicated content, which is content
indicated by the user; and a server receiving the information transmitted
from the client and recommending, to the user, content that is similar to
at least one of content related to the indicated party, the indicated
content, and content liked by the user.

Description:

BACKGROUND

[0001] The present disclosure relates to an information processing
apparatus, an information processing system, an information processing
method, and a program, and in particular to an information processing
apparatus, an information processing system, an information processing
method, and a program that are favorably used when recommending content.

[0002] In the past, a technology for providing user evaluations of songs
to a system, generating a taste vector for each user, and providing song
lists in keeping with each user's taste based on the similarity between
such taste vectors and characteristic vectors of individual songs has
been proposed (see, for example, WO2011007631). By using such technology,
it is possible for users to passively enjoy songs that match their tastes
without having to search through a huge list of songs by themselves.

SUMMARY

[0003] However, with the invention disclosed in the cited publication,
even if a user wishes to listen to songs by an artist who differs to the
user's usual taste, it is not possible at all times to provide the user
with songs by such artist. For example, if a user who usually likes to
listen to jazz wishes to listen to songs by an artist who is categorized
as rock, should the user request songs by such artist, the song list will
be generated based on a taste vector that reflects the user's historic
taste. Accordingly, in some cases songs by the artist requested by the
user may not be included in the song list, or may not be placed at or
near the top of the song list.

[0004] The present disclosure aims to provide content in keeping with a
user request.

[0005] According to an first embodiment of the present disclosure, there
is provided a device which includes an information processing apparatus
including an acquisition unit acquiring information showing at least one
of an indicated party, who is a person or group indicated by a user, and
indicated content, which is content indicated by the user, and a
recommendation unit recommending, to the user, content that is similar to
at least one of content related to the indicated party, the indicated
content, and content liked by the user.

[0006] The information processing apparatus may further include a vector
combining unit generating a combined vector by combining an indicated
characteristic vector, which is a characteristic vector showing
characteristics of the content related to the indicated party or the
indicated content, and a user taste vector, which shows characteristics
of the content liked by the user. The recommendation unit may recommend,
to the user, content whose characteristic vector is similar to the
combined vector.

[0007] The vector combining unit may combine the indicated characteristic
vector and the user taste vector using a ratio indicated by the user.

[0008] The information processing apparatus may further include a display
control unit controlling display of a setting screen which displays, when
the user has made a total of at least two indications of indicated
parties and/or indicated content, display respectively corresponding to
the indicated parties and/or the indicated content and a display
corresponding to the user at specified display positions and which sets a
ratio for use when combining the indicated characteristic vector and the
user taste vector, based on distances from the respective display
positions to a position indicated by the user.

[0009] The information processing apparatus may further include a display
control unit operable, when the indicated party has been indicated, to
carry out control to display a name of the indicated party in a setting
screen setting the ratio for use when combining the indicated
characteristic vector and the user taste vector. The vector combining
unit may combine the indicated characteristic vector showing the
characteristics of the content related to the indicated party and the
user taste vector using the ratio set in the setting screen.

[0011] The information processing apparatus may further include a
representative work extracting unit extracting a representative work out
of the content related to the indicated party based on at least one of
the number of multiple registrations of content and user evaluations of
content. The indicated characteristic vector generating unit may generate
the indicated characteristic vector for the indicated party based on a
characteristic vector of the extracted representative work.

[0012] The recommendation unit may generate a list of content recommended
to the user and set an order of content in the list based on similarity
between the combined vector and respective characteristic vectors of the
content.

[0013] The recommendation unit may generate a list of content recommended
to the user, and the information processing apparatus may further include
a representative work extracting unit extracting a representative work
out of content related to one of the indicated party and a person or
group related to the indicated content, based on at least one of the
number of multiple registrations of content and user evaluations of
content, and a representative work inserting unit inserting the extracted
representative work at or near the top of the list.

[0014] The representative work extracting unit may extract the
representative work separately for specified regions.

[0015] The information processing method carried out by the information
processing apparatus that recommends content, may include acquiring
information showing at least one of an indicated party, who is a person
or group indicated by a user, and indicated content, which is content
indicated by the user, and recommending, to the user, content that is
similar to at least one of content related to the indicated party, the
indicated content, and content liked by the user.

[0016] According to the first embodiment of the present disclosure, a
program causing a computer to execute processing may include acquiring
information showing at least one of an indicated party, who is a person
or group indicated by a user, and indicated content, which is content
indicated by the user, and recommending, to the user, content that is
similar to at least one of content related to the indicated party, the
indicated content, and content liked by the user.

[0017] According to a second embodiment of the present disclosure, there
is provided an information processing system including a server and a
client. The client may include a transmission unit transmitting
information showing at least one of an indicated party, who is a person
or group indicated by a user, and indicated content, which is content
indicated by the user, and the server may include or group indicated by a
user, and indicated content, which is content indicated by the user is
acquired and content that is similar to at least one of content related
to the indicated party, the indicated content, and content liked by the
user is recommended to the user.

[0018] According to the second embodiment of the present disclosure, there
is provided an information processing method including a client
transmitting information showing at least one of an indicated party, who
is a person or group indicated by a user, and indicated content, which is
content indicated by the user, and a server receiving the information
transmitted from the client and recommending, to the user, content that
is similar to at least one of content related to the indicated party, the
indicated content, and content liked by the user.

[0019] According to the first embodiment of the present disclosure,
information showing at least one of an indicated party, who is a person
or group indicated by a user, and indicated content, which is content
indicated by the user is acquired and content that is similar to at least
one of content related to the indicated party, the indicated content, and
content liked by the user is recommended to the user.

[0020] According to the second embodiment of the present disclosure,
information showing at least one of an indicated party, who is a person
or group indicated by a user, and indicated content, which is content
indicated by the user is transmitted by a client to a server, the
information transmitted from the client is received by the server, and
content that is similar to at least one of content related to the
indicated party, the indicated content, and content liked by the user is
recommended by the server to the user.

[0021] According to the first and second embodiments of the present
disclosure described above, it is possible to provide content in keeping
with a user request.

BRIEF DESCRIPTION OF THE DRAWINGS

[0022]FIG. 1 is a diagram showing the overall configuration of a content
recommendation system according to an embodiment of the present
disclosure;

[0023] FIG. 2 is a diagram showing the hardware configuration of a server;

[0024] FIG. 3 is a diagram showing the hardware configuration of a user
apparatus;

[0025] FIG. 4 is a perspective view showing the external appearance of a
user apparatus;

[0026] FIG. 5 is a perspective view showing the external appearance of a
user apparatus according to a modification;

[0027] FIG. 6 is a functional block diagram of a user apparatus;

[0028] FIG. 7 is a functional block diagram of a song distributing server;

[0034] FIG. 13 is a diagram schematically showing an example data
structure of a song evaluation database;

[0035] FIG. 14 is a functional block diagram of a recommendation unit;

[0036] FIG. 15 is a diagram showing the stored content of an internal
ranking storage unit;

[0037] FIG. 16 is a flowchart useful in explaining a representative song
extracting process;

[0038] FIG. 17 is a flowchart useful in explaining a song recommendation
process;

[0039] FIG. 18 is a diagram showing a first example of a setting screen
for setting a recommendation ratio;

[0040] FIG. 19 is a diagram showing a second example of a setting screen
for setting a recommendation ratio;

[0041] FIG. 20 is a flowchart useful in explaining a recommended song list
generating process; and

[0042] FIG. 21 is a diagram showing an example of a first list.

DETAILED DESCRIPTION OF THE EMBODIMENT(S)

[0043] Hereinafter, preferred embodiments of the present disclosure will
be described in detail with reference to the appended drawings. Note
that, in this specification and the appended drawings, structural
elements that have substantially the same function and structure are
denoted with the same reference numerals, and repeated explanation of
these structural elements is omitted.

[0044] Preferred embodiments of the present disclosure are described in
the order indicated below.

1. Embodiments

2. Modifications

1. First Embodiment

Example Configuration of Content Recommendation System 10

[0045]FIG. 1 is a diagram showing the overall configuration of a content
recommendation system 10 according to an embodiment of the present
disclosure.

[0046] The content recommendation system 10 includes a song distributing
server 14, a song ranking distributing server 15, and a plurality of user
apparatuses 12-1 to 12-n as clients. All of such apparatuses are
connected to a communication network 18, such as the Internet, and are
capable of data communication with one another.

[0047] Note that in the following description, when it is not necessary to
distinguish between the user apparatuses 12-1 to 12-n, such apparatuses
are collectively referred to as the "user apparatus 12".

[0048] As examples, the user apparatus 12 is constructed of a computer
system such as a personal computer, a computer game system, or a home
server set up in the home, or a portable computer system, such as a
mobile game console or a mobile phone. Each user apparatus 12 accesses
the song distributing server 14 and receives a list (hereinafter referred
to as a "recommended song list") of songs recommended to the user of that
particular user apparatus 12. Each user apparatus 12 also requests the
data of a song included in the recommended song list from the song
distributing server 14, and receives and reproduces such data.

[0049] Meanwhile, the song distributing server 14 is constructed of a
computer system or the like, such as a well-known server computer. The
song distributing server 14 transmits a list ("recommended song list") of
songs recommended to the user of a particular user apparatus 12 to such
user apparatus 12. The song distributing server 14 also transmits data of
individual songs in accordance with requests from the respective user
apparatuses 12.

[0050] As one example, the song ranking distributing server 15 is also
constructed of a computer system or the like, such as a well-known server
computer. The song ranking distributing server 15 is managed by a
different administrator to the song distributing server 14 and transmits
song rankings in response to requests from the song distributing server
14.

[0051] As one example, such song rankings are regularly issued (for
example, every week or every month) on a country-by-country basis for
individual music genres such as pop, jazz, and classical, and are stored
in association with the issue date and music genre in the song
distributing server 14. Note that such rankings may be generated from a
variety of viewpoints, and as one example may be based on number of
sales, number of downloads, and/or number of views of song-related
information (for example, a song description).

Example Configurations of Song Distributing Server 14 and Song Ranking
Distributing Server 15

[0052] FIG. 2 is a diagram showing example hardware configurations of the
song distributing server 14 and the song ranking distributing server 15.

[0053] The song distributing server 14 and/or the song ranking
distributing server 15 include a processor 21, a memory 22, a hard disk
drive 23, a medium drive 24, and a communication interface (I/F) 25, with
such component elements being connected to a bus 26 so as to be capable
of exchanging data with each other.

[0054] The processor 21 controls the various component elements of the
server in accordance with a program stored in the memory 22, the hard
disk drive 23, or a computer-readable medium 27.

[0055] The memory 22 is composed of ROM and RAM, for example, with various
system programs being stored in the ROM and the RAM mainly being used as
a workspace of the processor 21.

[0056] The hard disk drive 23 stores a program for distributing songs
and/or distributing song rankings and constructs various databases for
distributing songs and/or distributing song rankings.

[0057] The medium drive 24 is an apparatus that reads data stored on the
computer-readable medium 27, which is a CD-ROM, a DVD-RAM, or the like,
and/or writes data onto the computer-readable medium 27.

[0058] The communication interface 25 controls data communication via the
communication network 18 with another computer system such as a user
apparatus 12.

Example Configuration of User Apparatus 12

[0059] FIG. 3 is a diagram showing an example hardware configuration of
the user apparatus 12.

[0060] The user apparatus 12 includes a processor 31, a memory 32, a
display control unit 33, a sound control unit 34, a hard disk drive 35,
an operation device 36, a medium drive 37, and a communication interface
(I/F) 38, with such component elements being connected to a bus 39 so as
to be capable of exchanging data with each other.

[0061] The processor 31 controls the various component elements of the
user apparatus 12 in accordance with a program stored in the memory 32,
the hard disk drive 35, or a computer-readable medium 40.

[0062] The memory 32 is composed of ROM and RAM, for example, with various
system programs being stored in the ROM and the RAM mainly being used as
a workspace of the processor 31.

[0063] The display control unit 33 includes a video memory, converts
images drawn in the video memory by the processor 31 to a video signal,
and outputs the video signal to a display to have the images displayed.

[0064] The sound control unit 34 includes a sound buffer and converts
sound data stored in the sound buffer by the processor 31 to an analog
audio signal and outputs the analog audio signal to speakers to have
sound outputted.

[0065] The hard disk drive 35 stores various programs such as a song
reproduction program and constructs various databases.

[0066] The operation device 36 is used for example by the user to give
various instructions to the user apparatus 12 and to input data, and as
examples is constructed of a keyboard, a pointing device such as a mouse,
or a game pad.

[0067] The medium drive 37 is an apparatus that reads data stored on the
computer-readable medium 40, which is a CD-ROM, a DVD-RAM, or the like,
and/or writes data onto the computer-readable medium 40.

[0068] The communication interface 38 controls data communication via the
communication network 18 with another computer system such as the song
distributing server 14.

Specific Example of User Apparatus 12

[0069] The user apparatus 12 can be realized in a variety of forms, and
one example configuration shown in FIG. 4 is a home game console that
operates off a domestic power supply

[0070] In this case, the hardware elements shown in FIG. 3 are housed in a
case 43 and a display 41a and speakers 42, 42 of a television set 41 that
is separate to the case 43 are used as the display and speakers. The
operation device 36 is also provided separately to the case 43.

[0071] As an alternative, the user apparatus 12 can be configured as shown
in FIG. 5 as a portable all-in-one game console that operates off
batteries.

[0072] In this case, the hardware elements shown in FIG. 3 are housed in a
case 44 and a flat panel display 45 provided on the surface of the case
44 is used as the display. The operation device 36 is also provided on
the surface of the case 44 and as one example is disposed on the left and
right of the flat panel display 45. As the speakers, speakers, not shown,
incorporated in the case 44 may be used, as may be stereo headphones 46
that are separate to the case 44.

Example of Functional Configuration of User Apparatus 12

[0073] Here, the functional configuration of a user apparatus 12 will be
described. FIG. 6 is a functional block diagram of the user apparatus 12.

[0074] The user apparatus 12 functionally includes an operation unit 61
and a song reproducing unit 62. As one example, such functional elements
are realized by a program executed in the user apparatus 12.

[0075] The operation unit 61 is configured so as to be centered on the
operation device 36, and when a specified request operation has been
carried out on the operation device 36, a request for a recommended song
list (hereinafter referred to as a "song list request") is transmitted
via the communication interface 38 to the song distributing server 14.
The song list request includes a user ID that is identification
information of the user, an artist (hereinafter simply "indicated
artist"), a song (hereinafter simply "indicated song"), and an attribute
(hereinafter simply "indicated attribute") indicated by the user, and a
recommendation ratio, described later.

[0076] If the user has inputted an evaluation for a song using the
operation device 36, the operation unit 61 transmits user evaluation
information including a song ID that is identification information for
the song being evaluated, the user ID of the user making the evaluation,
and the inputted evaluation via the communication interface 38. As
examples, it is possible for the user to provide a positive evaluation
(for example, "like"), a negative evaluation (for example, "dislike") or
an evaluation value (for example, evaluation on five levels or a points
score) to each song.

[0077] The operation unit 61 also determines a user evaluation of a song
based on a user operation (as examples, skipping or stopping) carried out
on the operation device 36 during reproduction of the song and the
reproduced state of the song (as one example, whether the song was
reproduced to the end). The operation unit 61 then transmits user
evaluation information including the determined evaluation via the
communication interface 38.

[0078] In addition, if a user operation has been carried out for the
operation device 36, the operation unit 61 may notify the song
reproducing unit 62 of such operation as necessary.

[0079] The song reproducing unit 62 receives the recommended song list
transmitted from the song distributing server 14 via the communication
network 18 and the communication interface 38. In addition, the song
reproducing unit 62 transmits the song ID of each song included in the
recommended song list in order of the list via the communication
interface 38 to the song distributing server 14. The song reproducing
unit 62 receives song data transmitted from the song distributing server
14 in reply to transmission of a song ID via the communication network 18
and the communication interface 38 and reproduces the song data using the
sound control unit 34. At this time, as shown in FIGS. 4 and 5, the song
reproducing unit 62 displays the title of the song included in the song
data on the display. The song reproducing unit 62 also controls
reproduction of the song data in accordance with user operations of the
operation device 36.

Example of Functional Configuration of Song Distributing Server 14

[0080] Next, the functional configuration of the song distributing server
14 will be described. FIG. 7 is a functional block diagram of the song
distributing server 14.

[0081] In functional terms, the song distributing server 14 includes a
transmission/reception unit 101, a user information storage unit 102, a
song information storage unit 103, a totaling unit 104, a representative
song extracting unit 105, a representative song database 106, a vector
generating unit 107, a vector storage unit 108, a recommendation unit
109, a representative song inserting unit 110, a distribution unit 111,
and a display control unit 112. The vector generating unit 107 includes
an indicated characteristic vector generating unit 121, a user taste
vector generating unit 122, and a vector combining unit 123. As one
example, such functional elements are realized by a program being
executed in the song distributing server 14.

[0082] The transmission/reception unit 101 is configured so as to be
centered on the communication interface 25 and carries out data
communication via the communication network 18 with another computer
system such as a user apparatus 12. The transmission/reception unit 101
supplies received data to the various units of the song distributing
server 14 and transmits data acquired from the various units of the song
distributing server 14 to another computer system.

[0083] As one example, the transmission/reception unit 101 receives the
user evaluation information transmitted from the respective user
apparatuses 12 and supplies the user evaluation information to the
totaling unit 104. The transmission/reception unit 101 also receives song
rankings from the song ranking distributing server 15 and supplies the
song rankings to the recommendation unit 109.

[0084] In addition, the transmission/reception unit 101 receives song list
requests transmitted from the respective user apparatuses. The
transmission/reception unit 101 then notifies the indicated
characteristic vector generating unit 121 of the indicated artist and the
indicated song included in a song list request and requests generation of
an indicated characteristic vector. The transmission/reception unit 101
also notifies the user taste vector generating unit 122 of the user ID
included in a song list request and requests generation of a user taste
vector. In addition, the transmission/reception unit 101 notifies the
vector combining unit 123 of the recommendation ratio included in the
song list request. The transmission/reception unit 101 notifies the
recommendation unit 109 of the user ID and the indicated attribute
included in the song list request and requests generation of a
recommended song list. In addition, the transmission/reception unit 101
notifies the representative song inserting unit 110 of the indicated
artist and the indicated song included in the song list request.

[0085] The transmission/reception unit 101 transmits the recommended song
list supplied from the representative song inserting unit 110 to the user
apparatus 12 that issued the request. In addition, the
transmission/reception unit 101 notifies the totaling unit 104 of the
song IDs included in the recommended song list and the user ID of the
recipient of the recommended song list.

[0086] The transmission/reception unit 101 receives a song ID transmitted
from the user apparatus 12 and supplies the song ID to the distribution
unit 111. After this, the transmission/reception unit 101 acquires, from
the distribution unit 111, song data corresponding to the song ID
received from the user apparatus 12 and transmits the song data to the
user apparatus 12 that issued the request.

[0087] The user information storage unit 102 is configured using the hard
disk drive 23 or a separate database, not shown, and stores information
relating to the respective users of the content recommendation system 10.

[0088] As one example, the user information storage unit 102 stores a user
attribute database with the data structure schematically shown in FIG. 8.
The user attribute database is a database for managing the attributes of
the respective users and associates a user ID and attributes such as age,
location, language, and the like together. Note that the data in the user
attribute database is capable of being registered from the respective
user apparatuses 12.

[0089] The song information storage unit 103 is configured using the hard
disk drive 23 or a separate database, not shown, and stores information
relating to the songs distributed in the content recommendation system
10.

[0090] For example, the song information storage unit 103 stores the song
IDs and the data of the corresponding songs associated with one another.
Note that in cases such as when the same song is recorded on a plurality
of albums, a plurality of song data may be present for the same song. In
such case, a different song ID is assigned to each incidence of the song
data.

[0091] As one example, the song information storage unit 103 stores a song
information database with the data structure schematically shown in FIG.
9. The song information database is a database for managing information
relating to songs to be distributed, with a different database being
constructed for each country in which the services of the content
recommendation system 10 are provided. The song information database
associates each song ID with information relating to the song, such as
the song title, artist name, albums the song appears on, and the like.

[0092] In addition, as one example, the song information storage unit 103
stores a song characteristic database with the data structure
schematically shown in FIG. 10, for example. The song characteristic
database is a database for managing characteristic values expressing the
characteristics of songs. The song characteristic database associates the
song IDs with characteristic values for characteristics 1 to M of songs
corresponding to the song IDs. As the characteristics 1 to M, as
examples, the tempo of the song, the extent to which sounds of a
specified frequency are included in the song, the frequency with which a
specified keyword is included in the description text of the song, and
the like are used. Note that the characteristic values of each song may
be manually assigned or may be found by an analysis process carried out
by a computer.

[0093] Note that a vector that has the characteristic values of the
characteristics 1 to M as components and expresses the characteristics of
a song is called a "characteristic vector".

[0094] The song information storage unit 103 also stores a song attribute
database with the data structure schematically shown in FIG. 11, for
example. The song attribute database is a database for managing the
attributes of songs. In the song attribute database, the song IDs are
associated with flags showing whether the songs corresponding to the song
IDs have various attributes. As one example, the song attributes are song
moods such as "relaxed", "ballad", "happy", and "active" and are found
for example by an analysis process carried out by a computer.

[0095] The totaling unit 104 carries out totaling of user evaluation
information received from the respective user apparatuses 12 and
information relating to recommended song lists transmitted to the user
apparatuses 12. The totaling unit 104 includes a totaled information
storage unit 104a configured from the hard disk drive 23 or a separate
database, not shown, and stores the totaling results in the totaled
information storage unit 104a.

[0096] As one example, the totaled information storage unit 104a stores a
user evaluation database with the data structure schematically shown in
FIG. 12. The user evaluation database is a database that totals the
evaluations of songs by each user. In the user evaluation database, the
user ID is associated with song IDs of songs for which the user has given
a positive evaluation ("liked songs") and songs for which the user has
given a negative evaluation ("disliked songs").

[0097] In addition, the totaled information storage unit 104a stores a
song evaluation database with the data structure schematically shown in
FIG. 13, for example. The song evaluation database is a database in which
the evaluations of respective songs are totaled for each user attribute.
In the song evaluation database, the song IDs are associated with total
values showing evaluations of the songs for each user attribute.

[0098] The user attributes are classified according to a combination of
age, location, and language, for example. As one example the total values
are three values composed of a number of inclusions (x) in a song list
transmitted to a user apparatus 12, a number of transmissions (y) of
positive evaluations from user apparatuses 12 for the song, and a number
of transmissions (z) of negative evaluations from user apparatuses 12 for
the song.

[0099] The totaling unit 104 also requests the representative song
extracting unit 105 to extract representative songs of each artist at
specified timing.

[0100] As described later, the representative song extracting unit 105
extracts representative songs on a country-by-country basis for each
artist based on the song information database in the song information
storage unit 103 and the song evaluation database in the totaled
information storage unit 104a. The representative song extracting unit
105 registers the extracted representative songs in each country for each
artist in the representative song database 106.

[0101] The representative song database (DB) 106 is configured using the
hard disk drive 23 or a separate database, not shown, and has
representative songs in each country for each artist extracted by the
representative song extracting unit 105 registered therein.

[0102] As described later, the indicated characteristic vector generating
unit 121 generates an indicated characteristic vector showing
characteristics of songs of an artist indicated by a user based on the
song characteristic database in the song information storage unit 103 and
the representative song database 106. Alternatively, the indicated
characteristic vector generating unit 121 reads characteristic values of
a song indicated by the user from the song characteristic database in the
song information storage unit 103 to generate the indicated
characteristic vector. The indicated characteristic vector generating
unit 121 supplies the generated indicated characteristic vector to the
vector combining unit 123 and stores the indicated characteristic vector
in the vector storage unit 108.

[0103] As described later, the user taste vector generating unit 122 uses
the song characteristic database in the song information storage unit 103
and the user evaluation database in the totaled information storage unit
104a to generate user taste vectors expressing the characteristics of
songs liked by respective users. The user taste vector generating unit
122 also supplies the generated user taste vectors to the vector
combining unit 123 and stores the user taste vectors in the vector
storage unit 108.

[0104] As described later, based on the recommendation ratio indicated by
the user, the vector combining unit 123 combines the indicated
characteristic vector for an artist or song indicated by the user and the
user taste vector of the user to generate a combined vector. The vector
combining unit 123 also supplies the generated combined vector to the
recommendation unit 109 and stores the combined vector in the vector
storage unit 108.

[0106] As described later, the recommendation unit 109 generates a
recommended song list using the user attribute database in the user
information storage unit 102, the song attribute database and the song
characteristic database in the song information storage unit 103, the
song evaluation database in the totaled information storage unit 104a,
the song rankings received from the song ranking distributing server 15,
an indicated attribute indicated by the user, and the combined vector
generated by the vector combining unit 123. The recommendation unit 109
supplies the generated recommended song list to the representative song
inserting unit 110.

[0107] If an indicated song has been indicated by the user, the
representative song inserting unit 110 investigates the artist of the
indicated song based on the song information database in the song
information storage unit 103. The representative song inserting unit 110
extracts representative songs of an indicated artist indicated by the
user and the artist of an indicated song indicated by the user from the
representative song database 106 and inserts the representative songs at
or near the top of the recommended song list. The representative song
inserting unit 110 supplies the recommended song list after insertion of
the representative songs at or near the top to the transmission/reception
unit 101.

[0108] The distribution unit 111 receives a song ID transmitted from a
user apparatus 12 via the communication network 18 and the
transmission/reception unit 101. The distribution unit 111 acquires song
data related to the received song ID from the song information storage
unit 103 and transmits the song data via the transmission/reception unit
101 to the user apparatus 12 that issued the request.

[0109] As one example, the display control unit 112 controls the
displaying of screens that enable the user apparatus 12 to make use of
the services provided by the song distributing server 14. More
specifically, the display control unit 112 generates display control data
including a display program, data, and the like in accordance with
various requests received from the user apparatus 12 via the
communication network 18 and the transmission/reception unit 101 and
transmits the display control data via the transmission/reception unit
101 to the user apparatus 12. Based on the received display control data,
the user apparatus 12 displays a specified screen and/or updates the
displaying of a screen.

[0110] Note that although the various screens displayed on the user
apparatus 12 are divided into screens that are displayed based on display
control data supplied from the display control unit 112 of the song
distributing server 14 and screens that are displayed by the user
apparatus 12 by itself, the classification into such types can be set
arbitrarily.

Example Configuration of Recommendation Unit 109

[0111] Next, the functional configuration of the recommendation unit 109
of the song distributing server 14 will be described. FIG. 14 is a
functional block diagram of the recommendation unit 109.

[0113] Based on the song evaluation database in the totaled information
storage unit 104a, the internal ranking generating unit 151 regularly (as
examples, weekly or monthly) generates rankings (hereinafter referred to
as "internal rankings") of songs for a range of various user attributes.
The internal ranking generating unit 151 stores the generated internal
rankings in the internal ranking storage unit 152.

[0114] The internal ranking storage unit 152 is configured using the hard
disk drive 23 or a separate database, not shown. As shown in FIG. 15, the
internal ranking storage unit 152 stores various rankings generated by
the internal ranking generating unit 151 in association with the
generation time and a range of user attributes.

[0115] As one example, the ranking of songs liked by users who are fifteen
years old or under, whose location is Japan, and whose language is
Japanese is generated by placing the song IDs of a specified number of
songs (for example, 100) in descending order of the total number of
transmissions y of positive evaluations for each song recorded in the
columns "13 or under/Japan/Japanese", "14 y.o./Japan/Japanese", and "15
y.o./Japan/Japanese" in the song evaluation database in FIG. 13. At this
time, as one example, rankings may be generated by placing the song IDs
of a specified number of songs in order of the ratio of the total of y
(transmissions of positive evaluations) to the total of x (inclusions in
a song list) described above, that is, the ratio of a number of times a
positive evaluation has been given relative to the number of inclusions
in a recommended song list.

[0116] The ranking selection combining unit 153 reads out the user
attributes associated with the user ID included in the song list request
transmitted from a user apparatus 12 from the user attribute database in
the user information storage unit 102. The ranking selection combining
unit 153 also reads the internal rankings corresponding to the read out
user attributes from the internal ranking storage unit 152. In addition,
the ranking selection combining unit 153 receives song rankings
(hereinafter referred to as "external rankings") corresponding to the
user attributes via the transmission/reception unit 101 and the
communication network 18 from the song ranking distributing server 15.
After this, the ranking selection combining unit 153 generates a first
list by combining the song IDs included in the acquired two rankings. The
ranking selection combining unit 153 stores the generated first list in
the first list storage unit 154.

[0117] The first list storage unit 154 is configured using the hard disk
drive 23 or a separate database, not shown, and stores the first list.

[0118] The second list generating unit 155 reads out the first list from
the first list storage unit 154. The second list generating unit 155 then
narrows down the song IDs included in the first list based on the
indicated attribute included in the song list request and the song
attribute database in the song information storage unit 103 to generate
the second list. The second list generating unit 155 supplies the
generated second list to the sorting unit 156.

[0119] The sorting unit 156 sorts the song IDs of the second list based on
the combined vector supplied from the vector combining unit 123 and the
song characteristic database in the song information storage unit 103 to
generate the recommended song list. The sorting unit 156 supplies the
generated recommended song list to the representative song inserting unit
110.

Representative Song Extracting Process

[0120] Next, a representative song extracting process executed by the
content recommendation system 10 will be described with reference to the
flowchart in FIG. 16.

[0121] In step S1, the song distributing server 14 gathers evaluations for
songs from the respective users.

[0122] For example, the user is capable, during reproduction of a song, of
inputting an evaluation of the song being reproduced using the operation
device 36 of the user apparatus 12. If an evaluation has been inputted by
the user, the operation unit 61 of the user apparatus 12 transmits user
evaluation information showing the song ID of the song being reproduced,
the user ID, and the inputted evaluation via the communication interface
38 to the song distributing server 14.

[0123] Note that this operation is not limited to songs being reproduced
and it may also be possible to select a song not being reproduced, input
an evaluation for the selected song, and transmit user evaluation
information for such evaluation from the user apparatus 12 to the song
distributing server 14.

[0124] Also, as one example, if a skip operation has been made for the
operation device 36 during reproduction of a song, the operation unit 61
notifies the song reproducing unit 62. In accordance with such
notification, the song reproducing unit 62 cancels the reproduction of
the song, transmits the next song ID to the song distributing server 14,
and reproduces the song data sent in reply. At this time, the operation
unit 61 transmits user evaluation information showing the song ID of the
song that has been skipped, the user ID, and a negative evaluation via
the communication interface 38 to the song distributing server 14.

[0125] In addition, as one example, when a song has been reproduced to the
end without skipping, the song reproducing unit 62 notifies the operation
unit 61. In this case, the operation unit 61 transmits user evaluation
information showing the song ID of the song that has been reproduced to
the end, the user ID, and a positive evaluation via the communication
interface 38 to the song distributing server 14.

[0126] The transmission/reception unit 101 of the song distributing server
14 receives the user evaluation information transmitted from the
respective user apparatuses 12 via the communication network 18 as
described above and supplies the user evaluation information to the
totaling unit 104. The totaling unit 104 updates the totaling results
stored in the totaled information storage unit 104a based on the acquired
user evaluation information.

[0127] For example, when the user evaluation information shows a positive
evaluation, in the user evaluation database in FIG. 12, the totaling unit
104 adds the song ID shown in the user evaluation information to the
liked songs for the user ID shown in the user evaluation information.
Meanwhile, when the user evaluation information shows a negative
evaluation, in the user evaluation database in FIG. 12, the totaling unit
104 adds the song ID shown in the user evaluation information to the
disliked songs for the user ID shown in the user evaluation information.

[0128] Also, the totaling unit 104 reads out the attributes of the user
corresponding to the user ID shown in the user evaluation information
from the user attribute database in the user information storage unit
102. In the song evaluation database in FIG. 13, the totaling unit 104
then updates the totals of the user attribute range to which the user
attribute belongs out of the totals for the song ID shown in the user
evaluation information. More specifically, if a positive evaluation is
shown in the user evaluation information, the totaling unit 104 adds one
to the total y of transmissions of positive evaluations, and if a
negative evaluation is shown in the user evaluation information, the
totaling unit 104 adds one to the total z of transmissions of negative
evaluations.

[0129] The totaling unit 104 requests the representative song extracting
unit 105 to extract representative songs of each artist at specified
timing (as examples, at a specified time, at specified intervals, or when
a specified amount of user evaluation information has been stored). The
processing then proceeds to step S2.

[0130] In step S2, the representative song extracting unit 105 totals the
number of multiple registrations of each song on a country-by-country
basis. More specifically, the representative song extracting unit 105
extracts songs where the combination of title and artist name is the same
from the song information database for each country in the song
information storage unit 103 and counts the number of times the same
combination is registered (hereinafter, the number of "multiple
registrations") for the extracted songs. By doing so, the number of
multiple registrations of songs are totaled on a country-by-country
basis.

[0131] As examples, the representative songs of artists will normally be
recorded not only on the original album on which such songs first appear
but also multiple times on other albums such as a greatest hits album, a
live album, a remastered album, and a compilation album. Accordingly, it
can be assumed that there will be many multiple registrations of the
representative songs of each artist.

[0132] In step S3, the representative song extracting unit 105 totals the
evaluations of each song on a country-by-country basis. For example, the
representative song extracting unit 105 refers to the song evaluation
database in the totaled information storage unit 104a and totals the
numbers of the positive evaluations and the negative evaluations for each
song on a country-by-country basis. At this time, the representative song
extracting unit 105 combines the totaling results for the songs (songs
with the same artist and title but with different song IDs) registered
multiple times.

[0133] In step S4, the representative song extracting unit 105 extracts
the representative songs of each artist. More specifically, first the
representative song extracting unit 105 selects the artist (hereinafter
referred to as the "target artist") and the country (hereinafter referred
to as the "target country") to be used in the extraction. Next, the
representative song extracting unit 105 places the songs of the target
artist in order of number of multiple registrations for the target
country and, according to a specified standard, assigns points
(hereinafter referred to as "registration points") so that the higher a
song is ranked, the higher the points. Accordingly, the greater the
number of multiple registrations of a song, the larger the number of
registration points assigned to the song.

[0134] The representative song extracting unit 105 also places the songs
of the target artist in order of number of positive evaluations for the
target country or in order of the ratio of positive evaluations and,
according to a specified standard, assigns points (hereinafter referred
to as "evaluation points") so that the higher a song is ranked, the
higher the points. Accordingly, the greater the number of positive
evaluations given to a song that is popular, the larger the number of
registration points assigned to the song.

[0135] Note that the registration points and the evaluation points are
normalized so that the maximum value and/or a standard value are the
same, for example.

[0136] Next, the representative song extracting unit 105 weights and adds
the registration points and the evaluation points to calculate the
overall points of each song.

[0137] Note that the weights are variable and the values are adjusted
according to whether representative songs are being extracted with
emphasis on the number of multiple registrations or on user evaluations.

[0138] After this, the representative song extracting unit 105 extracts a
specified number of songs with high overall points as the representative
songs for the target artist in the target country. By doing so, songs
that are recorded on a larger number of albums and have been highly
evaluated by users are extracted as the representative songs for the
target country.

[0139] The representative song extracting unit 105 carries out such
processing for every artist and for every country. By doing so, the
representative songs in each country for each artist are extracted.

[0140] The representative song extracting unit 105 then updates the
representative songs in each country for each artist that are registered
in the representative song database 106.

[0141] After this, the representative song extracting process ends.

[0142] For example, if representative songs of an artist are extracted
manually, it is necessary to assemble a team of evaluators who are
informed about music. The chosen songs will also reflect the taste of
such evaluators, so there is no guarantee that representative songs will
be extracted objectively. In addition, when a number of evaluators are
used, there is the risk of the individual evaluators using different
evaluation standards. Also, the larger the number of songs, the larger
the number of required evaluators and the larger the task of evaluating
songs. In addition, the workload of the evaluators increases every time a
new song is added.

[0143] Although it would be conceivable to extract representative songs
based on sales, when extraction is carried out based on album sales, all
of the songs included in an album will be extracted as representative
songs. Also, when songs are extracted based on sales of singles, songs
that were not released as singles are be extracted as representative
songs.

[0144] Meanwhile, as described earlier, the representative songs of
respective artists are normally recorded multiple times on many albums
and as a result, such songs become registered multiple times in the song
information database. Accordingly, by using the number of multiple
registrations in a song information database, it is possible to extract
the representative songs of each artist objectively without needing the
extraction to be performed manually

[0145] However, since it can be envisaged that the number of multiple
registrations will be higher for older songs, such as debut songs, if
only the number of multiple registrations is used, there will be the risk
that the extracted representative songs will be biased toward old songs.
Also, for an artist who has released few albums, such as an artist with a
short career, there will be no difference in the number of multiple
registrations for songs, making it difficult to extract representative
songs.

[0146] For this reason, by extracting the representative songs using not
only the number of multiple registrations but also user evaluations, it
is possible to extract the representative songs more accurately in every
case. For example, there is a tendency for the number of evaluations
given for songs to increase faster for newer songs than for older songs.
Accordingly, it becomes possible to extract new songs that are highly
popular with users as the representative songs. It also becomes possible
to extract representative songs for artists with few album releases for
whom there is little difference between songs in the number of multiple
registrations.

[0147] In addition, by extracting the representative songs for respective
countries based on totaling results on a country-by-country basis as
described earlier, it is possible to cope with a case where the
representative songs differ between countries, such as when different
songs have been hits in different countries. It is also possible to cope
with a case where the songs that can be distributed differ on a
country-by-country basis due to copyright reasons or the like.

[0148] Note that as necessary, it is also possible to extract the
representative songs using only the registration points (i.e., based on
only the number of multiple registrations) and to extract the
representative songs using only the evaluation points (i.e., based on
only user evaluations).

Song Recommendation Process

[0149] Next, a song recommendation process carried out by the content
recommendation system 10 will be described with reference to the
flowchart in FIG. 17.

[0150] In step S51, a user apparatus 12 acquires a request from a user.

[0151] More specifically, if the user wishes to have songs distributed
from the song distributing server 14, the user uses the operation device
36 to input a request for the distribution of songs. At this time, the
user indicates an attribute (for example, a song mood such as "relaxed",
"ballad", "happy", and "active") of the songs the user wishes to have
distributed. Note that it is also possible for the attribute of the songs
to be randomly selected by the user apparatus 12 without being indicated
by the user.

[0152] The user also indicates an artist (indicated artist) or song
(indicated song) the user wishes to have distributed.

[0153] In addition, the user sets a recommendation ratio for indicating
the ratio of songs related to the indicated artist or indicated song to
songs that match the user's taste for use when the song distributing
server 14 recommends songs.

[0154] FIG. 18 shows one example of a setting screen for the
recommendation ratio. In this setting screen, "Artist A", which is the
name of the indicated artist, is displayed at the left end of a slide bar
201 as a display corresponding to the indicated artist indicated by the
user. "You" is displayed at the right end of the slide bar 201 as a
display corresponding to the user himself/herself. The recommendation
ratio is set based on the distance between the display positions of
"Artist A" and "You" and the position of a cursor 201a indicated by the
user.

[0155] More specifically, the closer the cursor 201a to the "Artist A"
side, the higher the recommendation ratio is set for artist A. As a
result, a recommended song list that does not strongly reflect the user's
taste and has many songs that are typically related to artist A placed at
or near the top of the list is distributed.

[0156] Meanwhile, the closer the cursor 201a to the "You" side, the higher
the recommendation ratio is set for the user's taste. As a result, a
recommended song list that does not strongly reflect the characteristics
of artist A and has many songs that match the user's taste placed at or
near the top of the list is distributed.

[0157] Also, the closer the cursor 201a to the midway point between
"Artist A" and "You", the closer the values set for the recommendation
ratio for artist A and the recommendation ratio for the user's taste. As
a result, a recommended song list which has many songs that are related
to artist A and match the user's taste placed at or near the top of the
list is distributed.

[0158] Here, the expression "songs related to an artist" includes not only
songs by the artist in question but also songs by other artists with
characteristics that are similar to the songs of the artist in question.
As examples, the latter may include songs by artists who have influenced
or been influenced by the artist in question, songs by artists with a
close relationship to the artist in question, and songs by artists of the
same genre as the artist in question.

[0159] Also, when a song has been indicated instead of indicating an
artist, the title of the indicated song is displayed on the setting
screen in FIG. 18 in place of the artist name.

[0160] The closer the cursor 201a to the title of the indicated song, the
higher the recommendation ratio is set for the indicated song. As a
result, a recommended song list that does not strongly reflect the user's
taste and normally has many songs related to the indicated song placed at
or near the top of the list is distributed.

[0161] Meanwhile, the closer the cursor 201a to the "You" side, the higher
the recommendation ratio is set for the user's taste. As a result, a
recommended song list that does not strongly reflect the characteristics
of the indicated song and has many songs that match the user's taste
placed at or near the top of the list is distributed.

[0162] Also, the closer the cursor 201a to the midway point between the
title of the indicated song and "You", the closer the values set for the
recommendation ratio for the indicated song and the recommendation ratio
for the user's taste. As a result, a recommended song list which has many
songs that are related to the indicated song and match the user's taste
placed at or near the top of the list is distributed.

[0163] Here, the expression "songs related to the indicated song" includes
not only songs by the artist of the indicated song but also songs with
characteristics that are similar to the indicated song.

[0164] Note that the number of indicated artists or indicated songs is not
limited to one and it is also possible to indicate two or more artists,
two or more songs, or a combination of songs and artists.

[0165] FIG. 19 shows an example of a setting screen for the recommendation
ratio in a case where two or more indications of artists and songs are
given. Note that FIG. 19 shows an example of a setting screen for the
recommendation ratio in a case where two artists are indicated.

[0166] In this setting screen, "You" is displayed near the top vertex of a
triangular menu 211 as a display corresponding to the user
himself/herself. "Artist A" and "Artist B" that are the names of the
indicated artists are displayed near the bottom left and bottom right
vertices of the menu 211 as displays corresponding to the indicated
artists that have been indicated by the user. A recommendation ratio is
set based on the distance between the position of a cursor 211a indicated
by the user and the respective display positions of "Artist A", "Artist
B", and "You".

[0167] More specifically, the closer the cursor 211a to "You", the higher
the recommendation ratio is set for the user's taste. Meanwhile, the
closer the cursor 211a to "Artist A", the higher the recommendation ratio
is set for artist A, and the closer the cursor 211a to "Artist B", the
higher the recommendation ratio is set for artist B.

[0168] Note that the user is also capable of simultaneously indicating an
indicated artist and an indicated song. For example, it is possible to
indicate artist A and to also indicate a song C of a different artist B
to artist A.

[0169] The operation unit 61 then acquires a song distribution request
inputted by the user.

[0170] In step S52, the operation unit 61 requests transmission of a
recommended song list. More specifically, the operation unit 61 generates
a song list request corresponding to the user request and transmits the
song list request via the communication interface 38 to the song
distributing server 14. The song list request includes a user ID, at
least one of an indicated artist and an indicated song, an indicated
attribute, and a recommendation ratio.

[0171] In step S53, the song distributing server 14 generates an indicated
characteristic vector. More specifically, the transmission/reception unit
101 of the song distributing server 14 receives a song list request from
the user apparatus 12 via the communication network 18. The
transmission/reception unit 101 then notifies the indicated
characteristic vector generating unit 121 of the indicated artist and the
indicated song included in the song list request and requests the
indicated characteristic vector generating unit 121 to generate an
indicated characteristic vector.

[0172] On being notified of an indicated artist, the indicated
characteristic vector generating unit 121 reads the representative songs
of the indicated artist from the representative song database 106. Also,
the indicated characteristic vector generating unit 121 reads
characteristic values of the read representative songs from the song
characteristic database in the song information storage unit 103. The
indicated characteristic vector generating unit 121 then generates the
indicated characteristic vector for the indicated artist based on the
characteristic values of the read representative songs. As one example,
the indicated characteristic vector generating unit 121 calculates the
average value for each characteristic out of the characteristic values of
the read representative songs and generates a vector with the calculated
average values as components as an indicated characteristic vector.

[0173] Note that it is not necessary to use all of the representative
songs of the indicated artist and as one example it is also possible to
generate the indicated characteristic vector by selecting a specified
number of songs from the representative songs. As another example, it is
also possible to generate the indicated characteristic vector by
selecting a specified number of songs by the indicated artist at random
without being limited to the representative songs. In addition, it is
possible for example to generate the indicated characteristic vector
using every song by the indicated artist.

[0174] Note that as the number of songs used increases, it becomes
increasingly likely that an indicated characteristic vector in which the
characteristic values of the respective songs are neutralized will be
generated, resulting in the risk that the particular characteristics of
the artist will no longer be reflected. Accordingly, it is desirable to
not use an excessively large number of songs.

[0175] On being notified of an indicated song, the indicated
characteristic vector generating unit 121 reads the characteristic values
of the indicated song from the song characteristic database in the song
information storage unit 103. The indicated characteristic vector
generating unit 121 then generates a vector with the read characteristic
values as components as the indicated characteristic vector.

[0176] Note that when a plurality of indicated artists or indicated songs
have been indicated, the indicated characteristic vector generating unit
121 generates an indicated characteristic vector for each artist or for
each song.

[0178] Note that it is possible to store the generated indicated
characteristic vectors in the vector storage unit 108 and to use the
indicated characteristic vectors stored in the vector storage unit 108
the next time the same artist or song is indicated.

[0179] In step S54, the song distributing server 14 generates a user taste
vector. More specifically, the transmission/reception unit 101 of the
song distributing server 14 notifies the user taste vector generating
unit 122 of the user ID included in the song list request and requests
generation of a user taste vector.

[0180] The user taste vector generating unit 122 reads the song IDs of
liked songs associated with the notified user ID from the user evaluation
database in the totaled information storage unit 104a. The user taste
vector generating unit 122 reads characteristic values of the read song
IDs from the song characteristic database in the song information storage
unit 103. The user taste vector generating unit 122 then uses the same
method as when generating the indicated characteristic vectors to
generate a user taste vector based on the characteristic values of the
read songs. The user taste vector generating unit 122 then supplies the
generated user taste vector to the vector combining unit 123.

[0181] In step S55, the vector combining unit 123 combines the two types
of vectors. More specifically, the vector combining unit 123 acquires a
recommendation ratio included in the song list request from the
transmission/reception unit 101. After this, the vector combining unit
123 weights and adds the indicated characteristic vector and the user
taste vector based on Equation (1) below to generate a combined vector.

[0182] Here, w is the weight and is set in a range of 0 to 1 based on the
recommendation ratio. Accordingly, the combined vector is a vector where
the indicated characteristic vector and the user taste vector are
combined with an estimated ratio effectively indicated by the user.

[0183] Note that the value of the weight w is set higher the larger the
recommendation ratio for the indicated artist or the indicated song, and
as a result, the combined vector becomes closer to the indicated
characteristic vector. Meanwhile, the value of the weight w is set lower
the smaller the recommendation ratio for the indicated artist or the
indicated song, and as a result, the combined vector becomes closer to
the user taste vector.

[0184] Note that when a plurality of indicated artists and indicated songs
have been indicated, a weight w is set for every indicated characteristic
vector based on the recommendation ratio for the respective indicated
artists and indicated songs. The indicated characteristic vectors and the
user taste vector are then combined using the respective weights w.

[0186] In step S56, the recommendation unit 109 carries out a recommended
song list generating process.

[0187] Here, the recommended song list generating process in step S56 will
now be described in detail with reference to the flowchart in FIG. 20.

[0188] In step S101, the ranking selection combining unit 153 acquires the
user attributes. More specifically, the transmission/reception unit 101
notifies the ranking selection combining unit 153 of the user ID included
in the song list request and requests combining of rankings. The ranking
selection combining unit 153 reads the user attributes corresponding to
the notified user ID from the user attribute database in the user
information storage unit 102.

[0189] In step S102, the ranking selection combining unit 153 acquires
internal rankings corresponding to the user attributes. That is, the
ranking selection combining unit 153 reads out internal rankings
corresponding to a range of user attributes including the read user
attributes from the internal ranking storage unit 152. Note that when
doing so, internal rankings corresponding to a range adjacent to the
range of user attributes of the read internal rankings may also be read
out.

[0190] In step S103, the ranking selection combining unit 153 acquires the
external rankings corresponding to the user attributes. That is, the
ranking selection combining unit 153 receives the external rankings
corresponding to the read user attributes via the transmission/reception
unit 101 and the communication network 18 from the song ranking
distributing server 15. For example, the ranking selection combining unit
153 receives the latest rankings for the location (country) of the user
or, based on the age of the user, receives rankings issued at such
location for a case where the user is fifteen years old.

[0191] In step S104, the ranking selection combining unit 153 combines the
acquired rankings. More specifically, as one example, the ranking
selection combining unit 153 generates a list ("first list") in which the
song IDs included in the acquired internal rankings and the song IDs
included in the external rankings are combined, as schematically shown in
FIG. 21. Note that at this time, it is not necessary for every song ID
included in the respective rankings to be included in the first list. The
ranking selection combining unit 153 stores the generated first list in
the first list storage unit 154.

[0192] In step S105, the second list generating unit 155 further narrows
the selection of songs based on the attributes of the songs. More
specifically, the transmission/reception unit 101 notifies the second
list generating unit 155 of the indicated attribute included in the song
list request and requests generation of the recommended song list. The
second list generating unit 155 reads the first list generated by the
ranking selection combining unit 153 from the first list storage unit
154. Also, the second list generating unit 155 reads the song attributes
associated with the respective song IDs included in the first list from
the song attribute database in the song information storage unit 103. In
addition, the second list generating unit 155 extracts the song IDs with
the indicated attribute out of the song IDs included in the first list.
The second list generating unit 155 then generates a second list composed
of the extracted song IDs. The second list generating unit 155 supplies
the generated second list to the sorting unit 156.

[0193] In step S106, the sorting unit 156 stores the song order using the
combined vector. More specifically, the sorting unit 156 reads the
characteristic values associated with the song IDs included in the second
list from the song characteristic database in the song information
storage unit 103. The sorting unit 156 then calculates the similarity
between a characteristic vector composed of the characteristic values of
a song ID and the combined vector, and sorts the song IDs in the second
list in descending order of similarity. By doing so, (song IDs of) songs
with characteristics that are similar to the characteristics of songs
shown by the combined vector are disposed at or near the top of the
second list.

[0194] Accordingly, as one example, the closer the weight w in Equation
(1) to 1, the more typical songs that are related to the indicated artist
or the indicated song are disposed at or near the top of the list
irrespective of the user's taste.

[0195] Meanwhile, the closer the weight w in Equation (1) to 0, the more
songs that match the user's taste are disposed at or near the top of the
list irrespective of the indicated artist or the indicated song.

[0196] Also, the closer the weight w in Equation (1) to 0.5, the more
songs that are related to the indicated artist or the indicated song and
match the user's taste are disposed at or near the top of the list.

[0197] In addition, the sorting unit 156 adjusts the order of the songs as
necessary so that the interval between songs of the same artist is a
predetermined number of songs or more. By doing so, it is possible to
prevent the song list from becoming monotonous due to songs from the same
artist being consecutively played. Also for Internet radio or the like,
if there is a restriction such that songs by the same artist are not to
be played without an interval of at least a specified number of songs in
between, it is possible to satisfy such restriction.

[0198] The second list generating unit 155 then supplies the second list
after sorting to the representative song inserting unit 110 as the
recommended song list.

[0199] After this the recommended song list generating process ends.

[0200] Returning to FIG. 17, in step S57, the representative song
inserting unit 110 inserts the representative songs at or near the top of
the recommended song list. More specifically, the representative song
inserting unit 110 acquires information showing the indicated artist
included in the song list request from the transmission/reception unit
101. The representative song inserting unit 110 also reads the
representative songs of the indicated artist from the representative song
database 106.

[0201] As one example, the representative song inserting unit 110 then
rearranges the song order between songs of the indicated artist so that
the representative songs of the indicated artist are disposed as close as
possible to the top of the recommended song list. For example, if a song
A that differs to the representative songs of the indicated artist is
disposed above the representative song B, the order of the song A and the
representative song B are interchanged.

[0202] If there is a representative song that is not included in the
recommended song list, the representative song inserting unit 110 also
adds (the song ID of) such representative song to the recommended song
list. As examples, a representative song is added to the recommended song
list by simply inserting (the song ID of) such representative song at or
near the top of the recommended song list, replacing a song that is not a
representative song of the indicated artist, or replacing a song of
another artist.

[0203] Note that if an indicated song is included in the song list
request, by carrying out the same processing, the indicated song and
representative songs of the artist of the indicated song are inserted at
or near the top of the recommended song list.

[0204] In step S58, the representative song inserting unit 110 transmits
the recommended song list via the transmission/reception unit 101 to the
user apparatus 12 that issued the request.

[0205] At this time, the transmission/reception unit 101 notifies the
totaling unit 104 of the song IDs included in the recommended song list
and the user ID who is the recipient of the recommended song list. The
totaling unit 104 reads the attributes of the user associated with such
user ID from the user attribute database in the user information storage
unit 102. The totaling unit 104 then adds one to the number of inclusions
x in a song list for the user attribute range to which the read
attributes belong corresponding to the song IDs in the song evaluation
database in the totaled information storage unit 104a.

[0206] In step S59, the song reproducing unit 62 of the user apparatus 12
receives the recommended song list via the communication network 18 and
the communication interface 38.

[0207] In step S60, the song reproducing unit 62 requests transmission of
song data. More specifically, the song reproducing unit 62 transmits the
highest song ID in the order out of the song IDs of songs yet to be
reproduced in the recommended song list via the communication interface
38 to the song distributing server 14.

[0208] In step S61, the song distributing server 14 sends the song data in
reply. More specifically, the distribution unit 111 of the song
distributing server 14 receives the song ID transmitted from the user
apparatus 12 via the communication network 18 and the
transmission/reception unit 101. The distribution unit 111 acquires the
song data associated with the received song ID from the song information
storage unit 103 and transmits the song data via the
transmission/reception unit 101 to the user apparatus 12 that issued the
request.

[0209] In step S62, the user apparatus 12 reproduces the song data. More
specifically, the song reproducing unit 62 of the user apparatus 12
receives the song data transmitted from the song distributing server 14
via the communication network 18 and the communication interface 38. The
song reproducing unit 62 then reproduces the received song data.

[0210] In step S63, the song reproducing unit 62 determines whether all of
the songs included in the recommended song list have been reproduced. If
it is determined that not all of the songs included in the recommended
song list have been reproduced, the processing returns to step S60.

[0211] After this, the processing in steps S60 to S63 is repeatedly
carried out until it is determined in step S63 that all of the songs
included in the recommended song list have been reproduced. By doing so,
songs corresponding to every song ID included in the recommended song
list are reproduced in the song order of the list.

[0212] Meanwhile, if it is determined in step S63 that every song included
in the recommended song list has been reproduced, processing ends.

[0213] Note that after every song included in the recommended song list
has been reproduced, it is also possible to return to step S51, and start
the processing again from step S51.

[0214] As described above, it is possible to recommend songs while giving
priority to songs that are similar to at least one of songs of the artist
indicated by the user, the song indicated by the user, and songs that the
user likes. For example, in addition to songs that match the user's
taste, it is possible to indicate an artist or a song that differs to the
user's normal taste and immediately recommend songs related to such
indicated artist or song.

[0215] Also, since songs are recommended while giving priority to
representative songs of the indicated artist, it is possible to prevent
recommending minor songs by the indicated artist that are not recognized
by most people.

[0216] In addition, since it is possible to arbitrarily adjust the
recommendation ratio, the user is capable of acquiring a recommended song
list that gives priority to songs related to the indicated artist or song
and is also capable of acquiring a recommended song list that gives
priority to songs that match the user's taste.

[0217] Also, since information on the indicated artist or song is not
reflected in the user taste vector, such indication will not affect the
songs recommended to the user thereafter. Accordingly, it is possible for
the user to receive pinpoint recommendations of songs that differ to the
user's taste and to prevent the undesired recommending of similar songs
in the future.

[0218] In addition, since the recommended song list is generated based on
various types of rankings that vary over time, a situation where the same
songs are continuously recommended to users is prevented and a variety of
songs can be recommended to the user.

2. Modifications

[0219] Modifications to the embodiment of the present disclosure will now
be described.

Modification 1

[0220] Although an example where recommended songs are extracted based on
rankings of songs is described in the above example, it is also possible
to extract songs according to another method.

[0221] For example, songs may be extracted randomly or songs whose
characteristic vectors are similar to the combined vector may be
extracted. In the latter case, as one example, it is possible to generate
a recommended song list composed of songs that are very similar and
provide such list to the user.

[0222] Also, the present disclosure may also be applied to simply
extracting songs whose characteristic vectors are similar to the combined
vector without generating a recommended song list, and recommending such
songs to the user.

Modification 2

[0223] Also, as the opposite of the example described above, it is also
possible to place songs related to the indicated artist or indicated song
at or near the bottom of a recommended song list, or to omit such songs
from a recommended song list. As one example, this could be conceivably
realized by using a combined vector produced by combining an inverse
vector for the indicated characteristic vector and the user taste vector.

Modification 3

[0224] In addition, although an example where representative songs are
extracted on a country-by-country basis has been described above, such
extraction is not limited to a country-by-country basis. For example,
representative songs may be extracted in a region composed of a plurality
of countries such as North America, the European Union (EU), or the like,
or may be extracted on a regional basis within the same country, such as
for the states, counties, prefectures, and regions.

Modification 4

[0225] Also, as one example, it is possible to indicate a person or group
related to a song aside from the artist and receive recommendations of
songs. Conceivable examples of such person or group include the
songwriter, lyricist, arranger, and producer. Also, the expression
"person or group" here is not limited to actual people and could
conceivably include a corporation or the like such as a record company, a
record label, or a music production company.

[0226] In such case, as examples, an indicated characteristic vector may
be generated and representative songs may be extracted from the songs
related to the indicated person or group based on the characteristic
values of songs related to the indicated person or group instead of the
artist.

Modification 5

[0227] In addition, as one example, each user apparatus 12 may acquire the
characteristic vector of each song from the song distributing server 14
and generate the user taste vector at the user apparatus 12. As one
example, the user apparatus 12 may then include the user taste vector in
a song list request and send such song list request to the song
distributing server 14.

Modification 6

[0228] As one example, it is also possible to provide an indicated
characteristic vector generated by another apparatus to the song
distributing server 14 without having an indicated characteristic vector
generated at the song distributing server 14.

Modification 7

[0229] In addition, it is possible to register an artist or song,
recommendation ratio, indicated attribute, or the like indicated by the
user in the song distributing server 14.

[0230] As one example, when the user likes a recommended song list that
has been provided, the indicated artist or song, recommendation ratio,
and indicated attribute are registered in the song distributing server
14. A user can then use the registered information to easily receive
provision of the same recommended song list, even at a different user
apparatus 12.

Modification 8

[0231] In addition, means for analyzing the characteristic values of a
song may be provided in the song distributing server 14.

Modification 9

[0232] Also, the present disclosure can be applied to a case where various
types of content are recommended, such as video like a movie or a
television program, a still image such as a photograph or a painting, an
electronic book, game, or a document file.

[0233] In this case, in the same way as songs, it is possible to indicate
a person or group related to such content or to indicate the content
itself and receive recommendations of content. Also, the person or group
indicated by the user may differ according to the type of content, with
conceivable examples being various kinds of artists and writers, such as
a movie director, actor, writer, painter, artist, photographer,
performer, designer, or creator. The "person or group" is not limited to
actual people and could conceivably include a corporation such as a movie
studio, a television station, a manufacturer, or a brand.

[0234] In addition, in the same way as with songs, it is possible to
extract representative works by an indicated person or group based on the
number of multiple registrations and user evaluations and to extract
representative works using another viewpoint. Also, the characteristic
values for the content in use may be changed as appropriate according to
the type of content.

[0235] The series of processes described above can be executed by hardware
but can also be executed by software. When the series of processes is
executed by software, a program that constructs such software is
installed into a computer. Here, the expression "computer" includes a
computer in which dedicated hardware is incorporated and a
general-purpose personal computer or the like that is capable of
executing various functions when various programs are installed.

[0236] It should be noted that the program executed by a computer may be a
program that is processed in time series according to the sequence
described in this specification or a program that is processed in
parallel or at necessary timing such as upon calling.

[0237] In the present specification, the expression "system" is assumed to
mean an apparatus or collection of apparatuses composed of a plurality of
apparatuses, means, or the like.

[0238] It should be understood by those skilled in the art that various
modifications, combinations, sub-combinations and alterations may occur
depending on design requirements and other factors insofar as they are
within the scope of the appended claims or the equivalents thereof.

[0239] Moreover, the present technology can also be configured as below,
for example.

(1)

[0240] An information processing apparatus including:

[0241] an acquisition unit acquiring information showing at least one of
an indicated party, who is a person or group indicated by a user, and
indicated content, which is content indicated by the user; and

[0242] a recommendation unit recommending, to the user, content that is
similar to at least one of content related to the indicated party, the
indicated content, and content liked by the user.

(2)

[0243] An information processing apparatus according to (1), further
including:

[0244] a vector combining unit generating a combined vector by combining
an indicated characteristic vector, which is a characteristic vector
showing characteristics of the content related to the indicated party or
the indicated content, and a user taste vector, which shows
characteristics of the content liked by the user,

[0245] wherein the recommendation unit recommends, to the user, content
whose characteristic vector is similar to the combined vector.

(3)

[0246] An information processing apparatus according to (2),

[0247] wherein the vector combining unit combines the indicated
characteristic vector and the user taste vector using a ratio indicated
by the user.

(4)

[0248] An information processing apparatus according to (3), further
including:

[0249] a display control unit controlling display of a setting screen
which displays, when the user has made a total of at least two
indications of indicated parties and/or indicated content, displays
respectively corresponding to the indicated parties and/or the indicated
content and a display corresponding to the user at specified display
positions and which sets a ratio for use when combining the indicated
characteristic vector and the user taste vector, based on distances from
the respective display positions to a position indicated by the user.

(5)

[0250] An information processing apparatus according to (3), further
including:

[0251] a display control unit operable, when the indicated party has been
indicated, to carry out control to display a name of the indicated party
in a setting screen setting the ratio for use when combining the
indicated characteristic vector and the user taste vector,

[0252] wherein the vector combining unit combines the indicated
characteristic vector showing the characteristics of the content related
to the indicated party and the user taste vector using the ratio set in
the setting screen.

(6)

[0253] An information processing apparatus according to any of (2) to (5),
further including:

[0256] An information processing apparatus according to (6), further
including:

[0257] a representative work extracting unit extracting a representative
work out of the content related to the indicated party based on at least
one of the number of multiple registrations of content and user
evaluations of content,

[0258] wherein the indicated characteristic vector generating unit
generates the indicated characteristic vector for the indicated party
based on a characteristic vector of the extracted representative work.

(8)

[0259] An information processing apparatus according to any of (2) to (7),

[0260] wherein the recommendation unit generates a list of content
recommended to the user and sets an order of content in the list based on
similarity between the combined vector and respective characteristic
vectors of the content.

(9)

[0261] An information processing apparatus according to any of (1) to (6),

[0262] wherein the recommendation unit generates a list of content
recommended to the user, and

[0263] the information processing apparatus further includes

[0264] a representative work extracting unit extracting a representative
work out of content related to one of the indicated party and a person or
group related to the indicated content, based on at least one of the
number of multiple registrations of content and user evaluations of
content; and

[0265] a representative work inserting unit inserting the extracted
representative work at or near the top of the list.

(10)

[0266] An information processing apparatus according to (7) or (9),

[0267] wherein the representative work extracting unit extracts the
representative work separately for specified regions.

(11)

[0268] An information processing method carried out by an information
processing apparatus that recommends content, including:

[0269] acquiring information showing at least one of an indicated party,
who is a person or group indicated by a user, and indicated content,
which is content indicated by the user; and

[0270] recommending, to the user, content that is similar to at least one
of content related to the indicated party, the indicated content, and
content liked by the user.

(12)

[0271] A program causing a computer to execute processing including:

[0272] acquiring information showing at least one of an indicated party,
who is a person or group indicated by a user, and indicated content,
which is content indicated by the user; and

[0273] recommending, to the user, content that is similar to at least one
of content related to the indicated party, the indicated content, and
content liked by the user.

(13)

[0274] An information processing system including a server and a client,

[0275] wherein the client includes a transmission unit transmitting
information showing at least one of an indicated party, who is a person
or group indicated by a user, and indicated content, which is content
indicated by the user; and

[0276] the server includes

[0277] a reception unit receiving the information transmitted from the
client; and

[0278] a recommendation unit recommending, to the user, content that is
similar to at least one of content related to the indicated party, the
indicated content, and content liked by the user.

(14)

[0279] An information processing method including:

[0280] a client transmitting information showing at least one of an
indicated party, who is a person or group indicated by a user, and
indicated content, which is content indicated by the user; and

[0281] a server receiving the information transmitted from the client and
recommending, to the user, content that is similar to at least one of
content related to the indicated party, the indicated content, and
content liked by the user.

[0282] The present disclosure contains subject matter related to that
disclosed in Japanese Priority Patent Application JP 2011-159543 filed in
the Japan Patent Office on Jul. 21, 2011, the entire content of which is
hereby incorporated by reference.